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Approximate Bayesian Computation and MCMC

In: Monte Carlo and Quasi-Monte Carlo Methods 2002

Author

Listed:
  • Vincent Plagnol

    (University of Southern California, Program in Molecular and Computational Biology)

  • Simon Tavaré

    (University of Southern California, Program in Molecular and Computational Biology)

Abstract

Summary For many complex probability models, computation of likelihoods is either impossible or very time consuming. In this article, we discuss methods for simulating observations from posterior distributions without the use of likelihoods. A rejection approach is illustrated using an example concerning inference in the fossil record. A novel Markov chain Monte Carlo approach is also described, and illustrated with an example from population genetics.

Suggested Citation

  • Vincent Plagnol & Simon Tavaré, 2004. "Approximate Bayesian Computation and MCMC," Springer Books, in: Harald Niederreiter (ed.), Monte Carlo and Quasi-Monte Carlo Methods 2002, pages 99-113, Springer.
    • Handle: RePEc:spr:sprchp:978-3-642-18743-8_5
    DOI: 10.1007/978-3-642-18743-8_5
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